{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jPI3U6mnP6x_"
      },
      "source": [
        "Copyright 2023 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    https://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qw7dxETZUkg"
      },
      "source": [
        "# Colab to run MADLAD models\n",
        "\n",
        "Adapted from [T5X tutorial](https://colab.research.google.com/github/google-research/t5x/blob/main/t5x/notebooks/inference.ipynb#scrollTo=f9_BPXG_QgJs)\n",
        "\n",
        "*Special thanks to Juarez Bochi*\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqZYp90PIa1t"
      },
      "source": [
        "## Install packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M23bwrwVVYtL",
        "outputId": "8305bdb7-ba6d-457a-e026-5849fc5286a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 't5x'...\n",
            "remote: Enumerating objects: 6284, done.\u001b[K\n",
            "remote: Counting objects: 100% (158/158), done.\u001b[K\n",
            "remote: Compressing objects: 100% (90/90), done.\u001b[K\n",
            "remote: Total 6284 (delta 82), reused 103 (delta 66), pack-reused 6126\u001b[K\n",
            "Receiving objects: 100% (6284/6284), 9.38 MiB | 3.58 MiB/s, done.\n",
            "Resolving deltas: 100% (4461/4461), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/google-research/t5x.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p10CTfDTcqEb",
        "outputId": "1a102ea2-88e3-4f33-d194-f44e957e9b9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Successfully installed jax-0.4.20 jaxlib-0.4.20 ml-dtypes-0.3.1 numpy-1.26.1 opt-einsum-3.3.0 scipy-1.11.3\n",
            "Processing /usr/local/google/home/biaojiaxing/work/tmp/jupyter-server/t5x\n",
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            "\u001b[?25hCollecting airio@ git+https://github.com/google/airio#egg=airio (from t5x==0.0.0)\n",
            "  Cloning https://github.com/google/airio to /tmp/pip-install-aawkrx82/airio_b13fee94cd1449d09f54f56a8c0c3345\n",
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            "\u001b[?25hCollecting flax@ git+https://github.com/google/flax#egg=flax (from t5x==0.0.0)\n",
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            "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Installing backend dependencies ... \u001b[?25l-\b \b\\\b \bdone\n",
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            "\u001b[?25hUsing cached zipp-3.17.0-py3-none-any.whl (7.4 kB)\n",
            "Downloading cachetools-5.3.2-py3-none-any.whl (9.3 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: t5x, airio, clu, flax, jestimator, optax, seqio, ml_collections, promise\n",
            "  Building wheel for t5x (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25h  Created wheel for t5x: filename=t5x-0.0.0-py3-none-any.whl size=537539 sha256=a82724c7878f5136e8e4390f96556c73f7777e6d206c3ade1e3dfdd0d0b7c6d4\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/8c/5e/f7/a203ceeee7df469882dcaab822728183bf86a6843d58295968\n",
            "  Building wheel for airio (setup.py) ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Created wheel for airio: filename=airio-0.0.1-py3-none-any.whl size=52299 sha256=413138cda2cfa0681b48459c8a645f2b4ef5bc82f617924219280656bb832595\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/78/97/3e/060f3045a4aedeb17b240430126be515c0fc6d55808195c2e4\n",
            "  Building wheel for clu (setup.py) ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Created wheel for clu: filename=clu-0.0.11-py3-none-any.whl size=100847 sha256=28d7a44bc2933dcd8f9d8e4f903f160789737b5c006a95cf434dc38f5574e1ec\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/39/c1/24/1c54cf87b5c57f73c428681b03e228d5187b900bbd1c33b90f\n",
            "  Building wheel for flax (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Created wheel for flax: filename=flax-0.8.0-py3-none-any.whl size=244220 sha256=49d9eb924adc532907f7a5f6955df3564dfe5ab89274bd9dcbb6650d330a92da\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/c2/b5/6c/e10d1bcdb3a30e0ffa048cf3a43762bb6f93c8262a27dd9a5d\n",
            "  Building wheel for jestimator (pyproject.toml) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for jestimator: filename=jestimator-0.3.3-py3-none-any.whl size=2114058 sha256=4b93b8450e6d2bc76667fc810df11f133ef8765c386729c64334bcdabd5557f6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/0e/85/c0/a42e70edeb24559d5bb400f24e4f2220f99ad02c1ef7ceaac6\n",
            "  Building wheel for optax (pyproject.toml) ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Created wheel for optax: filename=optax-0.1.8.dev0-py3-none-any.whl size=175556 sha256=23566c0d7afbb0c0f4a1f88a44263c38f7417b6703e81ae5b1f3f80f6c4d5538\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/23/21/3e/1d698479dc8dc59adfe13d4a2983e18cb2b95a251bcfde9718\n",
            "  Building wheel for seqio (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Created wheel for seqio: filename=seqio-0.0.17-py3-none-any.whl size=352935 sha256=7cc8f34359925a4d4c65f6501ab91f041d9fa3aa320b0d98fa31daf550030c03\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-3xvz7uo5/wheels/f3/82/f4/d9062a177e9f3274845c1f495e749c4f943f963ab5ca5836c0\n",
            "  Building wheel for ml_collections (setup.py) ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Created wheel for ml_collections: filename=ml_collections-0.1.1-py3-none-any.whl size=94506 sha256=f99fea0a15a36f2d472475918a47103facdeba9627e3d705c7ccd08114fe06f5\n",
            "  Stored in directory: /usr/local/google/home/biaojiaxing/.cache/pip/wheels/7b/89/c9/a9b87790789e94aadcfc393c283e3ecd5ab916aed0a31be8fe\n",
            "  Building wheel for promise (setup.py) ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21484 sha256=312b2567b50454fae5a98851a903de0752a082a9ce52967dc676137b9df19a36\n",
            "  Stored in directory: /usr/local/google/home/biaojiaxing/.cache/pip/wheels/54/4e/28/3ed0e1c8a752867445bab994d2340724928aa3ab059c57c8db\n",
            "Successfully built t5x airio clu flax jestimator optax seqio ml_collections promise\n",
            "Installing collected packages: sentencepiece, libclang, gin-config, flatbuffers, dm-tree, cached_property, zipp, wrapt, werkzeug, typing-extensions, typeguard, tqdm, toolz, toml, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, promise, oauthlib, numpy, mypy-extensions, msgpack, more-itertools, mdurl, markdown, keras, importlib_resources, grpcio, graphviz, google-pasta, gast, fsspec, etils, editdistance, docstring-parser, contextlib2, cloudpickle, click, cachetools, astunparse, absl-py, typing-inspect, tensorflow-hub, rsa, requests-oauthlib, pyglove, pyasn1-modules, ml_collections, markdown-it-py, jaxtyping, h5py, googleapis-common-protos, tensorstore, tensorflow-metadata, rich, libcst, google-auth, orbax-checkpoint, google-auth-oauthlib, fiddle, chex, array-record, tensorboard, optax, grain-nightly, tfds-nightly, tensorflow-cpu, tensorflow, flax, tensorflow-text, jestimator, clu, seqio, airio, t5x\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.8.0\n",
            "    Uninstalling typing_extensions-4.8.0:\n",
            "      Successfully uninstalled typing_extensions-4.8.0\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.26.1\n",
            "    Uninstalling numpy-1.26.1:\n",
            "      Successfully uninstalled numpy-1.26.1\n",
            "Successfully installed absl-py-1.4.0 airio-0.0.1 array-record-0.5.0 astunparse-1.6.3 cached_property-1.5.2 cachetools-5.3.2 chex-0.1.84 click-8.1.7 cloudpickle-3.0.0 clu-0.0.11 contextlib2-21.6.0 dm-tree-0.1.8 docstring-parser-0.15 editdistance-0.6.2 etils-1.5.2 fiddle-0.2.11 flatbuffers-23.5.26 flax-0.8.0 fsspec-2023.10.0 gast-0.4.0 gin-config-0.5.0 google-auth-2.23.4 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 googleapis-common-protos-1.61.0 grain-nightly-0.0.2 graphviz-0.20.1 grpcio-1.59.2 h5py-3.10.0 importlib_resources-6.1.0 jaxtyping-0.2.23 jestimator-0.3.3 keras-2.13.1 libclang-16.0.6 libcst-1.1.0 markdown-3.5.1 markdown-it-py-3.0.0 mdurl-0.1.2 ml_collections-0.1.1 more-itertools-10.1.0 msgpack-1.0.7 mypy-extensions-1.0.0 numpy-1.24.3 oauthlib-3.2.2 optax-0.1.8.dev0 orbax-checkpoint-0.4.1 promise-2.3 protobuf-3.20.3 pyasn1-0.5.0 pyasn1-modules-0.3.0 pyglove-0.4.3 requests-oauthlib-1.3.1 rich-13.6.0 rsa-4.9 sentencepiece-0.1.99 seqio-0.0.17 t5x-0.0.0 tensorboard-2.13.0 tensorboard-data-server-0.7.2 tensorflow-2.13.1 tensorflow-cpu-2.13.1 tensorflow-estimator-2.13.0 tensorflow-hub-0.15.0 tensorflow-io-gcs-filesystem-0.34.0 tensorflow-metadata-1.14.0 tensorflow-text-2.13.0 tensorstore-0.1.47 termcolor-2.3.0 tfds-nightly-4.9.2.dev202308090034 toml-0.10.2 toolz-0.12.0 tqdm-4.66.1 typeguard-2.13.3 typing-extensions-4.5.0 typing-inspect-0.9.0 werkzeug-3.0.1 wrapt-1.15.0 zipp-3.17.0\n"
          ]
        }
      ],
      "source": [
        "cpu = True  # True\n",
        "if cpu:\n",
        "  !python -m pip uninstall -y jax jaxlib\n",
        "  !python -m pip install jax[cpu]\n",
        "  !cd t5x \u0026\u0026 pip install .\n",
        "else:\n",
        "  !cd t5x \u0026\u0026 python3 -m pip install -e '.[tpu]' -f https://storage.googleapis.com/jax-releases/libtpu_releases.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaiG9mn_Q3w9",
        "outputId": "857d9e65-0bee-42c5-ff1e-e60555d6a36d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Collecting seqio\n",
            "  Using cached seqio-0.0.18-py3-none-any.whl.metadata (51 kB)\n",
            "Collecting t5\n",
            "  Using cached t5-0.9.4-py2.py3-none-any.whl (164 kB)\n",
            "Collecting numpy==1.24.1\n",
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            "\u001b[?25hCollecting tensorstore==0.1.35\n",
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            "Requirement already satisfied: clu in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (0.0.11)\n",
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            "Requirement already satisfied: jax in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (0.4.20)\n",
            "Requirement already satisfied: jaxlib in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (0.4.20)\n",
            "Requirement already satisfied: packaging in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (23.2)\n",
            "Requirement already satisfied: pyglove in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (0.4.3)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (0.1.99)\n",
            "Requirement already satisfied: tensorflow-text in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (2.13.0)\n",
            "Requirement already satisfied: tfds-nightly in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (4.9.2.dev202308090034)\n",
            "Requirement already satisfied: protobuf\u003c=3.20.3 in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from seqio) (3.20.3)\n",
            "Requirement already satisfied: babel in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from t5) (2.13.1)\n",
            "Collecting immutabledict (from t5)\n",
            "  Downloading immutabledict-3.0.0-py3-none-any.whl.metadata (3.1 kB)\n",
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            "Collecting rouge-score\u003e=0.1.2 (from t5)\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\n",
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            "  Downloading scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/google/home/biaojiaxing/work/conda/miniconda3/envs/jupyter/lib/python3.10/site-packages (from t5) (1.11.3)\n",
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            "Collecting transformers\u003e=2.7.0 (from t5)\n",
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            "\u001b[?25hBuilding wheels for collected packages: rouge-score, future\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24932 sha256=7331c4f0a659e367c01e6e22b69e97a1a887bcf5b4a9870c53dd133310e6c6f4\n",
            "  Stored in directory: /usr/local/google/home/biaojiaxing/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\n",
            "\u001b[?25h  Created wheel for future: filename=future-0.18.3-py3-none-any.whl size=492024 sha256=5c505da69eaf6dcf83f58940fa113a6a0ebea3783d010a60c76b531b319b388d\n",
            "  Stored in directory: /usr/local/google/home/biaojiaxing/.cache/pip/wheels/5e/a9/47/f118e66afd12240e4662752cc22cefae5d97275623aa8ef57d\n",
            "Successfully built rouge-score future\n",
            "Installing collected packages: pytz, tzdata, threadpoolctl, tabulate, safetensors, regex, portalocker, numpy, lxml, joblib, immutabledict, future, filelock, colorama, tensorstore, sacrebleu, pandas, nltk, mesh-tensorflow, huggingface-hub, tokenizers, scikit-learn, rouge-score, transformers, tensorflow-datasets, seqio-nightly, seqio, t5\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.24.3\n",
            "    Uninstalling numpy-1.24.3:\n",
            "      Successfully uninstalled numpy-1.24.3\n",
            "  Attempting uninstall: tensorstore\n",
            "    Found existing installation: tensorstore 0.1.47\n",
            "    Uninstalling tensorstore-0.1.47:\n",
            "      Successfully uninstalled tensorstore-0.1.47\n",
            "  Attempting uninstall: seqio\n",
            "    Found existing installation: seqio 0.0.17\n",
            "    Uninstalling seqio-0.0.17:\n",
            "      Successfully uninstalled seqio-0.0.17\n",
            "Successfully installed colorama-0.4.6 filelock-3.13.1 future-0.18.3 huggingface-hub-0.17.3 immutabledict-3.0.0 joblib-1.3.2 lxml-4.9.3 mesh-tensorflow-0.1.21 nltk-3.8.1 numpy-1.24.1 pandas-2.1.2 portalocker-2.8.2 pytz-2023.3.post1 regex-2023.10.3 rouge-score-0.1.2 sacrebleu-2.3.1 safetensors-0.4.0 scikit-learn-1.3.2 seqio-0.0.18 seqio-nightly-0.0.17.dev20231102 t5-0.9.4 tabulate-0.9.0 tensorflow-datasets-4.9.3 tensorstore-0.1.35 threadpoolctl-3.2.0 tokenizers-0.14.1 transformers-4.35.0 tzdata-2023.3\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade seqio t5 numpy==1.24.1 tensorstore==0.1.35"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbAhp0joefth",
        "outputId": "16d0defc-9196-495c-cfcf-8e9dfbdf0e4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_8.mlp.wo.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_8.pre_mlp_layer_norm.scale/0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_8.pre_attention_layer_norm.scale/0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.attention.key.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.attention.out.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.attention.query.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.attention.value.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.mlp.wi_0.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.mlp.wi_1.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.mlp.wo.kernel/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.pre_attention_layer_norm.scale/0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.layers_9.pre_mlp_layer_norm.scale/0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.encoder.relpos_bias.rel_embedding/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/0.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/1.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/11.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/12.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/10.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/14.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/13.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/15.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/2.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/3.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/4.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/5.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/6.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/7.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/8.0...\n",
            "Copying gs://madlad-400-checkpoints/checkpoints/3b-mt/target.token_embedder.embedding/9.0...\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Madlad checkpoint\n",
        "!gsutil -m cp -r gs://madlad-400-checkpoints/checkpoints/3b-mt ."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQgbbapGqHC1"
      },
      "source": [
        "## Rebuild .zarray files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vXs21LMReqyc"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import os\n",
        "import orbax.checkpoint\n",
        "\n",
        "orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n",
        "model = orbax_checkpointer.restore(\"3b-mt\")\n",
        "\n",
        "def create_jarr_files(d):\n",
        "  if not isinstance(d, dict):\n",
        "    return\n",
        "  if \"kvstore\" in d:\n",
        "    path = os.path.join('3b-mt', d[\"kvstore\"][\"path\"], \".zarray\")\n",
        "    with open(path, \"w\") as f:\n",
        "      data = d[\"metadata\"].copy()\n",
        "      data[\"zarr_format\"] = 2\n",
        "      assert d[\"dtype\"] == \"float32\"\n",
        "      data[\"dtype\"] = \"\u003cf4\"\n",
        "      data[\"dimension_separator\"] = \".\"\n",
        "      data[\"fill_value\"] = None\n",
        "      data[\"filters\"] = None\n",
        "      data[\"order\"] = \"C\"\n",
        "      f.write(json.dumps(data))\n",
        "  for v in d.values():\n",
        "    create_jarr_files(v)\n",
        "\n",
        "create_jarr_files(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZJbWZcfkyxI"
      },
      "source": [
        "## Set-Up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8my9yhSRi6GG"
      },
      "source": [
        "Note: If you are using the public colab (no the Pro version), you are likely run out of memory. Please consider using its `Connect to a local runtime` option by following the [setup guide](https://github.com/google-research/t5x/blob/main/t5x/notebooks/README.md)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jIGSIHzD7YPO"
      },
      "outputs": [],
      "source": [
        "\n",
        "import functools\n",
        "\n",
        "import jax\n",
        "from jax.experimental import multihost_utils\n",
        "import numpy as np\n",
        "import os\n",
        "import re\n",
        "import seqio\n",
        "import tensorflow as tf\n",
        "\n",
        "from t5x.examples.t5 import network\n",
        "import t5x\n",
        "from t5x import models\n",
        "from t5x import partitioning\n",
        "from t5x import trainer as trainer_lib\n",
        "from t5x import utils\n",
        "from t5x.infer import _extract_tokens_and_aux_values\n",
        "from t5x.interactive_model import get_dataset_from_natural_text_examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5Lb-Z1fkF5a"
      },
      "source": [
        "### Model Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ne8U8qoWkX_r"
      },
      "outputs": [],
      "source": [
        "# t5 network\n",
        "t5_config = network.T5Config(\n",
        "    vocab_size=256000,\n",
        "    dtype='bfloat16',\n",
        "    emb_dim=1024,\n",
        "    num_heads=16,\n",
        "    num_encoder_layers=32,\n",
        "    num_decoder_layers=32,\n",
        "    head_dim=128,\n",
        "    mlp_dim=8192,\n",
        "    mlp_activations=('gelu', 'linear'),\n",
        "    dropout_rate=0.0,\n",
        "    logits_via_embedding=False)\n",
        "module = network.Transformer(config=t5_config)\n",
        "\n",
        "# vocabulary\n",
        "vocabulary = seqio.SentencePieceVocabulary(\n",
        "    'gs://madlad-400-checkpoints/vocabulary/256k_vocab/spm.model')\n",
        "\n",
        "# model\n",
        "model = t5x.models.EncoderDecoderModel(\n",
        "    module=module,\n",
        "    input_vocabulary=vocabulary,\n",
        "    output_vocabulary=vocabulary,\n",
        "    optimizer_def=t5x.adafactor.Adafactor(decay_rate=0.8, step_offset=0))\n",
        "\n",
        "# downloaded checkpoint path\n",
        "checkpoint_path='3b-mt'\n",
        "\n",
        "# Misc\n",
        "dtype='bfloat16'\n",
        "restore_mode='specific'\n",
        "# Define a partitioner.\n",
        "partitioner=partitioning.PjitPartitioner(num_partitions=1)\n",
        "# Define additional, miscellaneous constructor arguments.\n",
        "batch_size=8\n",
        "task_feature_lengths = {'inputs': 64, 'targets': 64}\n",
        "output_dir='/tmp/output_dir'\n",
        "input_shapes = {\n",
        "    'encoder_input_tokens': np.array([8, 38]),\n",
        "    'decoder_target_tokens': np.array([8, 18]),\n",
        "    'decoder_input_tokens': np.array([8, 18]),\n",
        "    'decoder_loss_weights': np.array([8, 18])\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYwdg-fFTU8Q"
      },
      "source": [
        "### Checkpoint Restore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YmGTJBAcTpMR"
      },
      "outputs": [],
      "source": [
        "# 1.) Configure the Output Directory\n",
        "output_dir = re.sub(r\"(?\u003c!gs:)([\\/]{2,})\", \"/\", output_dir)\n",
        "if not os.path.exists(output_dir):\n",
        "  os.mkdir(output_dir)\n",
        "\n",
        "# 2.) Initialize RNGs\n",
        "init_random_seed = 42\n",
        "random_seed = multihost_utils.broadcast_one_to_all(np.int32(init_random_seed))\n",
        "utils.set_hardware_rng_ops()\n",
        "rng = jax.random.PRNGKey(random_seed)\n",
        "init_rng, trainer_rng = jax.random.split(rng, 2)\n",
        "\n",
        "# 3.) Validate the Partitioner\n",
        "if partitioner._model_parallel_submesh:\n",
        "  num_partitions = np.prod(partitioner._model_parallel_submesh)\n",
        "else:\n",
        "  num_partitions = partitioner._num_partitions\n",
        "if jax.device_count() % num_partitions != 0:\n",
        "  raise ValueError(\n",
        "    \"The number of devices available must be a multiple of the number of\",\n",
        "    f\" partitions. There are {jax.device_count()} devices available, but\",\n",
        "    f\" the number of partitions is set to {num_partitions}. Please\",\n",
        "    \" provide a different number of partitions.\")\n",
        "\n",
        "# 4.) Create a Checkpoint Manager\n",
        "# a.) Define CheckpointCfg wrappers.\n",
        "save_checkpoint_cfg = utils.SaveCheckpointConfig(\n",
        "        dtype=dtype,\n",
        "        keep=5, # The number of checkpoints to keep in the output_dir.\n",
        "        save_dataset=False)\n",
        "restore_checkpoint_cfg = utils.RestoreCheckpointConfig(\n",
        "        dtype=dtype,\n",
        "        mode=restore_mode,\n",
        "        path=checkpoint_path)\n",
        "\n",
        "# b.) Define a train state initializer, which will help us get information about the\n",
        "# TrainState shape.\n",
        "train_state_initializer = utils.TrainStateInitializer(\n",
        "        optimizer_def=model.optimizer_def,\n",
        "        init_fn=model.get_initial_variables,\n",
        "        input_shapes=input_shapes,\n",
        "        input_types=None,\n",
        "        partitioner=partitioner)\n",
        "\n",
        "# c.) Define the checkpoint manager.\n",
        "checkpoint_manager = utils.LegacyCheckpointManager(\n",
        "        save_cfg=save_checkpoint_cfg,\n",
        "        restore_cfg=restore_checkpoint_cfg,\n",
        "        train_state_shape=train_state_initializer.global_train_state_shape,\n",
        "        partitioner=partitioner,\n",
        "        ds_iter=None,\n",
        "        model_dir=output_dir)\n",
        "\n",
        "### 5.) Restore the Model from a Checkpoint, or Initialize from Scratch ###\n",
        "def get_state(rng):\n",
        "  return train_state_initializer.from_scratch(rng).state_dict()\n",
        "\n",
        "# a.) Try to restore a model from a checkpoint.\n",
        "train_state = checkpoint_manager.restore(\n",
        "  [restore_checkpoint_cfg.path],\n",
        "  restore_checkpoint_cfg,\n",
        "  utils.get_fallback_state(restore_checkpoint_cfg, get_state, init_rng)\n",
        ")\n",
        "\n",
        "# b.) If no checkpoint to restore, init from scratch.\n",
        "if train_state is None:\n",
        "  train_state = train_state_initializer.from_scratch(init_rng)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ib9aOi2xaCKQ"
      },
      "source": [
        "## Do the inference\n",
        "\n",
        "Some languages supported (with parallel data)\n",
        "\n",
        "ace\n",
        "ace_Arab\n",
        "af\n",
        "am\n",
        "an\n",
        "ar\n",
        "ary\n",
        "arz\n",
        "as\n",
        "az\n",
        "ba\n",
        "ban\n",
        "bar\n",
        "be\n",
        "bg\n",
        "bho\n",
        "bjn\n",
        "bjn_Arab\n",
        "bm\n",
        "bn\n",
        "br\n",
        "bs\n",
        "bug\n",
        "ca\n",
        "ceb\n",
        "crh_Latn\n",
        "cs\n",
        "cy\n",
        "da\n",
        "de\n",
        "din\n",
        "dv\n",
        "dz\n",
        "el\n",
        "en\n",
        "en_xx_simple\n",
        "eo\n",
        "es\n",
        "et\n",
        "eu\n",
        "fa\n",
        "fi\n",
        "fil\n",
        "fo\n",
        "fr\n",
        "fr_CA\n",
        "fr_ca\n",
        "fur\n",
        "fuv\n",
        "fy\n",
        "ga\n",
        "gd\n",
        "gl\n",
        "gn\n",
        "gu\n",
        "ha\n",
        "he\n",
        "hi\n",
        "hne\n",
        "hr\n",
        "hu\n",
        "hy\n",
        "id\n",
        "ig\n",
        "io\n",
        "is\n",
        "it\n",
        "iu\n",
        "ja\n",
        "jv\n",
        "ka\n",
        "kk\n",
        "km\n",
        "kn\n",
        "ko\n",
        "kr\n",
        "kr_Arab\n",
        "ks\n",
        "ks_Deva\n",
        "ku\n",
        "ky\n",
        "la\n",
        "lb\n",
        "li\n",
        "lij\n",
        "lmo\n",
        "lt\n",
        "ltg\n",
        "lv\n",
        "mag\n",
        "mg\n",
        "mi\n",
        "mk\n",
        "ml\n",
        "mn\n",
        "mni\n",
        "mr\n",
        "ms\n",
        "mt\n",
        "mwl\n",
        "my\n",
        "nb\n",
        "nds\n",
        "nds_NL\n",
        "nds_nl\n",
        "ne\n",
        "nl\n",
        "nn\n",
        "no\n",
        "nus\n",
        "oc\n",
        "or\n",
        "pa\n",
        "pl\n",
        "prs\n",
        "ps\n",
        "pt\n",
        "pt_br\n",
        "ro\n",
        "ru\n",
        "rw\n",
        "sc\n",
        "scn\n",
        "sd\n",
        "se\n",
        "sh\n",
        "shn\n",
        "si\n",
        "simple\n",
        "sk\n",
        "sl\n",
        "so\n",
        "sq\n",
        "sr\n",
        "sv\n",
        "sw\n",
        "szl\n",
        "ta\n",
        "taq\n",
        "taq_Tfng\n",
        "te\n",
        "tg\n",
        "th\n",
        "tk\n",
        "tl\n",
        "tr\n",
        "tt\n",
        "tzm\n",
        "ug\n",
        "uk\n",
        "ur\n",
        "uz\n",
        "vec\n",
        "vi\n",
        "wa\n",
        "wuu\n",
        "xh\n",
        "yi\n",
        "yo\n",
        "zh\n",
        "zh_Hant\n",
        "zh_cn\n",
        "zh_tw\n",
        "zu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IuFiyFqi5wCi"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhhR0yDcAn7w",
        "outputId": "1af679bf-d887-4c6b-f587-f11b32d0562b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:T5 library uses PAD_ID=0, which is different from the sentencepiece vocabulary, which defines pad_id=-1\n"
          ]
        }
      ],
      "source": [
        "examples = [\n",
        "    '\u003c2zh\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.',\n",
        "    '\u003c2es\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.',\n",
        "    '\u003c2de\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.',\n",
        "    '\u003c2en\u003e 11月1日起，四项电动车国家标准正式实施。这些标准对电动车的设计制造、安全性能、电池系统和标识等方面都提出了明确规定，涉及电动车的车把、锂电池、总线设计、电子控制系统等。',\n",
        "    '\u003c2en\u003e Ahora, un estudio de una universidad de Arizona ha revelado que usar redes luminosas puede resolver este problema.',\n",
        "]\n",
        "\n",
        "# tokenization\n",
        "output_features = {\n",
        "    \"inputs\":\n",
        "        seqio.Feature(\n",
        "            vocabulary=model.input_vocabulary, add_eos=True),\n",
        "    \"targets\":\n",
        "        seqio.Feature(\n",
        "            vocabulary=model.output_vocabulary, add_eos=True)\n",
        "}\n",
        "\n",
        "# datasetm, preprocessing and feature conversion\n",
        "dataset = get_dataset_from_natural_text_examples(\n",
        "    examples,\n",
        "    preprocessors=[\n",
        "        seqio.preprocessors.tokenize,\n",
        "        seqio.preprocessors.append_eos\n",
        "    ],\n",
        "    task_feature_lengths=task_feature_lengths,\n",
        "    features=output_features)\n",
        "feature_converter = model.FEATURE_CONVERTER_CLS(pack=False)\n",
        "model_dataset = feature_converter(\n",
        "    dataset, task_feature_lengths=task_feature_lengths)\n",
        "\n",
        "# inference function\n",
        "infer_fn = functools.partial(\n",
        "  utils.get_infer_fn(\n",
        "    infer_step=model.predict_batch_with_aux,\n",
        "    batch_size=batch_size,\n",
        "    train_state_axes=train_state_initializer.train_state_axes,\n",
        "    partitioner=partitioner),\n",
        "  train_state=train_state)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3l_Ihma2vI8O",
        "outputId": "d7ab27d1-1c77-412c-c3f0-02a9ed3e16d3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n",
            "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Source: \u003c2zh\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.\n",
            "Translation: 专家们认为,双方都重新提出了雄心壮志,并通过一系列姿态为访问扫清了道路。\n",
            "Score: -5.694250106811523\n",
            "\n",
            "Source: \u003c2es\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.\n",
            "Translation: Ahora ambas partes han renovado sus ambiciones y han despejado el camino para la visita con una serie de gestos, dicen los expertos.\n",
            "Score: -2.3198225498199463\n",
            "\n",
            "Source: \u003c2de\u003e Now both sides have renewed ambitions and have cleared the way for the visit with a series of gestures, experts say.\n",
            "Translation: Jetzt haben beide Seiten ihre Ambitionen erneuert und den Weg für den Besuch mit einer Reihe von Gesten frei gemacht, sagen Experten.\n",
            "Score: -3.43707537651062\n",
            "\n",
            "Source: \u003c2en\u003e 11月1日起，四项电动车国家标准正式实施。这些标准对电动车的设计制造、安全性能、电池系统和标识等方面都提出了明确规定，涉及电动车的车\n",
            "Translation: As of 1 November, four national standards for electric vehicles have been officially implemented.These standards provide clear regulations for the design and manufacture of electric vehicles, safety performance, battery systems and marking, and for vehicles involving electric vehicles.\n",
            "Score: -6.792211532592773\n",
            "\n",
            "Source: \u003c2en\u003e Ahora, un estudio de una universidad de Arizona ha revelado que usar redes luminosas puede resolver este problema.\n",
            "Translation: Now, a study from an Arizona university has revealed that using luminous nets can solve this problem.\n",
            "Score: -2.857039213180542\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n"
          ]
        }
      ],
      "source": [
        "# Infererence and get the predictions\n",
        "predictions, aux_values = _extract_tokens_and_aux_values(\n",
        "    infer_fn(model_dataset.enumerate()))\n",
        "\n",
        "inferences = []\n",
        "for idx, inputs in model_dataset.enumerate().as_numpy_iterator():\n",
        "  prediction = np.asarray(predictions[idx]).astype(np.int32)\n",
        "  source = np.asarray(inputs['encoder_input_tokens']).astype(np.int32)\n",
        "\n",
        "  # Remove padding values\n",
        "  source = source[source != 0]\n",
        "  prediction = prediction[prediction != 0]\n",
        "\n",
        "  # Decode into string\n",
        "  prediction = output_features[\"targets\"].vocabulary.decode_tf(\n",
        "      tf.constant(prediction)).numpy()\n",
        "  source = output_features[\"inputs\"].vocabulary.decode_tf(\n",
        "      tf.constant(source)).numpy()\n",
        "  inferences.append((source, prediction, aux_values['scores'][idx]))\n",
        "\n",
        "for src, hyp, score in inferences:\n",
        "  print(f\"Source: {src.decode('utf-8')}\")\n",
        "  print(f\"Translation: {hyp.decode('utf-8')}\")\n",
        "  print(f\"Score: {score}\")\n",
        "  print()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
